{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('font', **{'family':'Microsoft YaHei, SimHei'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_table('D:\\\\csdn-python\\\\第十五周 数据挖掘与机器学习基础（下）\\\\第二周作业\\\\log.txt', engine='python',names=['id','api','count','res_time_sum','res_time_min','res_time_max','res_time_avg','interval','created_at'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id                     api  count  res_time_sum  res_time_min  \\\n",
       "0  162542  /front-api/bill/create      8       1057.31         88.75   \n",
       "1  162644  /front-api/bill/create      5        749.12        103.79   \n",
       "2  162742  /front-api/bill/create      5        845.84        136.31   \n",
       "3  162808  /front-api/bill/create      9       1305.52         90.12   \n",
       "4  162943  /front-api/bill/create      3        568.89        138.45   \n",
       "\n",
       "   res_time_max  res_time_avg  interval           created_at  \n",
       "0        177.72         132.0        60  2017-11-01 00:00:07  \n",
       "1        240.38         149.0        60  2017-11-01 00:01:07  \n",
       "2        225.73         169.0        60  2017-11-01 00:02:07  \n",
       "3        196.61         145.0        60  2017-11-01 00:03:07  \n",
       "4        232.02         189.0        60  2017-11-01 00:04:07  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>179491</th>\n",
       "      <td>13438800</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>11</td>\n",
       "      <td>2783.48</td>\n",
       "      <td>99.24</td>\n",
       "      <td>489.90</td>\n",
       "      <td>253.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-05-30 23:06:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179492</th>\n",
       "      <td>13438866</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>10</td>\n",
       "      <td>1951.10</td>\n",
       "      <td>85.37</td>\n",
       "      <td>529.51</td>\n",
       "      <td>195.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-05-30 23:07:21</td>\n",
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       "    <tr>\n",
       "      <th>179493</th>\n",
       "      <td>13438917</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>494.17</td>\n",
       "      <td>103.95</td>\n",
       "      <td>211.47</td>\n",
       "      <td>164.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-05-30 23:08:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179494</th>\n",
       "      <td>13438981</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1798.28</td>\n",
       "      <td>101.11</td>\n",
       "      <td>433.30</td>\n",
       "      <td>199.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-05-30 23:09:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179495</th>\n",
       "      <td>13439086</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>6</td>\n",
       "      <td>1017.97</td>\n",
       "      <td>74.45</td>\n",
       "      <td>298.97</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-05-30 23:10:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                     api  count  res_time_sum  res_time_min  \\\n",
       "179491  13438800  /front-api/bill/create     11       2783.48         99.24   \n",
       "179492  13438866  /front-api/bill/create     10       1951.10         85.37   \n",
       "179493  13438917  /front-api/bill/create      3        494.17        103.95   \n",
       "179494  13438981  /front-api/bill/create      9       1798.28        101.11   \n",
       "179495  13439086  /front-api/bill/create      6       1017.97         74.45   \n",
       "\n",
       "        res_time_max  res_time_avg  interval           created_at  \n",
       "179491        489.90         253.0        60  2018-05-30 23:06:21  \n",
       "179492        529.51         195.0        60  2018-05-30 23:07:21  \n",
       "179493        211.47         164.0        60  2018-05-30 23:08:21  \n",
       "179494        433.30         199.0        60  2018-05-30 23:09:21  \n",
       "179495        298.97         169.0        60  2018-05-30 23:10:21  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.794960e+05</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.866490e+06</td>\n",
       "      <td>7.175909</td>\n",
       "      <td>1393.177370</td>\n",
       "      <td>108.419620</td>\n",
       "      <td>359.880351</td>\n",
       "      <td>187.812208</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.686579e+06</td>\n",
       "      <td>4.325160</td>\n",
       "      <td>1499.485881</td>\n",
       "      <td>79.640559</td>\n",
       "      <td>638.919769</td>\n",
       "      <td>224.464813</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.625420e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.825183e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>607.707500</td>\n",
       "      <td>83.410000</td>\n",
       "      <td>198.280000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.811432e+06</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1154.905000</td>\n",
       "      <td>97.120000</td>\n",
       "      <td>256.090000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.981397e+06</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1834.117500</td>\n",
       "      <td>116.990000</td>\n",
       "      <td>374.410000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.343909e+07</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>142650.550000</td>\n",
       "      <td>18896.640000</td>\n",
       "      <td>142468.270000</td>\n",
       "      <td>71325.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id          count   res_time_sum   res_time_min  \\\n",
       "count  1.794960e+05  179496.000000  179496.000000  179496.000000   \n",
       "mean   6.866490e+06       7.175909    1393.177370     108.419620   \n",
       "std    3.686579e+06       4.325160    1499.485881      79.640559   \n",
       "min    1.625420e+05       1.000000      36.550000       3.210000   \n",
       "25%    3.825183e+06       4.000000     607.707500      83.410000   \n",
       "50%    6.811432e+06       7.000000    1154.905000      97.120000   \n",
       "75%    9.981397e+06      10.000000    1834.117500     116.990000   \n",
       "max    1.343909e+07      31.000000  142650.550000   18896.640000   \n",
       "\n",
       "        res_time_max   res_time_avg  interval  \n",
       "count  179496.000000  179496.000000  179496.0  \n",
       "mean      359.880351     187.812208      60.0  \n",
       "std       638.919769     224.464813       0.0  \n",
       "min        36.550000      36.000000      60.0  \n",
       "25%       198.280000     144.000000      60.0  \n",
       "50%       256.090000     167.000000      60.0  \n",
       "75%       374.410000     202.000000      60.0  \n",
       "max    142468.270000   71325.000000      60.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(3,2,1)\n",
    "df[['count']].boxplot()\n",
    "plt.title('count')\n",
    "\n",
    "plt.subplot(3,2,2)\n",
    "df[['res_time_sum']].boxplot()\n",
    "plt.title('res_time_sum')\n",
    "\n",
    "plt.subplot(3,2,3)\n",
    "df[['res_time_min']].boxplot()\n",
    "plt.title('res_time_min')\n",
    "\n",
    "plt.subplot(3,2,4)\n",
    "df[['res_time_max']].boxplot()\n",
    "plt.title('res_time_max')\n",
    "\n",
    "plt.subplot(3,2,5)\n",
    "df[['res_time_avg']].boxplot()\n",
    "plt.title('res_time_avg')\n",
    "\n",
    "plt.subplot(3,2,6)\n",
    "df[['interval']].boxplot()\n",
    "plt.title('interval')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10.0, 1834.1175, 116.99, 374.41, 202.0)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q_upper,s_upper,i_upper,x_upper,a_upper = df['count'].quantile(0.75),df['res_time_sum'].quantile(0.75),df['res_time_min'].quantile(0.75),df['res_time_max'].quantile(0.75),df['res_time_avg'].quantile(0.75)\n",
    "q_upper,s_upper,i_upper,x_upper,a_upper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.0, 607.7075, 83.41, 198.28, 144.0)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q_lower,s_lower,i_lower,x_lower,a_lower = df['count'].quantile(0.25),df['res_time_sum'].quantile(0.25),df['res_time_min'].quantile(0.25),df['res_time_max'].quantile(0.25),df['res_time_avg'].quantile(0.25)\n",
    "q_lower,s_lower,i_lower,x_lower,a_lower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6.0, 1226.41, 33.58, 176.13000000000002, 58.0)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q_val,s_val,i_val,x_val,a_val = q_upper - q_lower,s_upper - s_lower,i_upper - i_lower,x_upper - x_lower,a_upper - a_lower\n",
    "q_val,s_val,i_val,x_val,a_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id              148012\n",
       "api             148012\n",
       "count           148012\n",
       "res_time_sum    148012\n",
       "res_time_min    148012\n",
       "res_time_max    148012\n",
       "res_time_avg    148012\n",
       "interval        148012\n",
       "created_at      148012\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df[(df['count'] > q_lower - q_val * k) & (df['count'] < q_upper + q_val * k) & (df['res_time_sum'] > s_lower - s_val * k) & (df['res_time_sum'] < s_upper + s_val * k) & (df['res_time_min'] > i_lower - i_val * k) & (df['res_time_min'] < i_upper + i_val * k) & (df['res_time_max'] > x_lower - x_val * k) & (df['res_time_max'] < x_upper + x_val * k) & (df['res_time_avg'] > a_lower - a_val * k) & (df['res_time_avg'] < a_upper + a_val * k)]\n",
    "df2.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "148011"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.duplicated(subset=['api','interval']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2017-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2017-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2017-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2017-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2017-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  count  res_time_sum  res_time_min  res_time_max  res_time_avg  \\\n",
       "0  162542      8       1057.31         88.75        177.72         132.0   \n",
       "1  162644      5        749.12        103.79        240.38         149.0   \n",
       "2  162742      5        845.84        136.31        225.73         169.0   \n",
       "3  162808      9       1305.52         90.12        196.61         145.0   \n",
       "4  162943      3        568.89        138.45        232.02         189.0   \n",
       "\n",
       "            created_at  \n",
       "0  2017-11-01 00:00:07  \n",
       "1  2017-11-01 00:01:07  \n",
       "2  2017-11-01 00:02:07  \n",
       "3  2017-11-01 00:03:07  \n",
       "4  2017-11-01 00:04:07  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2.drop(labels=['api','interval'],axis=1)\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:00:07</th>\n",
       "      <td>162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:01:07</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:02:07</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:03:07</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:04:07</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         id  count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                                     \n",
       "2017-11-01 00:00:07  162542      8       1057.31         88.75        177.72   \n",
       "2017-11-01 00:01:07  162644      5        749.12        103.79        240.38   \n",
       "2017-11-01 00:02:07  162742      5        845.84        136.31        225.73   \n",
       "2017-11-01 00:03:07  162808      9       1305.52         90.12        196.61   \n",
       "2017-11-01 00:04:07  162943      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg  \n",
       "created_at                         \n",
       "2017-11-01 00:00:07         132.0  \n",
       "2017-11-01 00:01:07         149.0  \n",
       "2017-11-01 00:02:07         169.0  \n",
       "2017-11-01 00:03:07         145.0  \n",
       "2017-11-01 00:04:07         189.0  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.to_datetime(df3.created_at)\n",
    "# df4 = df3.set_index('created_at')\n",
    "df3.index = s\n",
    "df4 = df3.drop('created_at', axis=1)\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.506676</td>\n",
       "      <td>595.017262</td>\n",
       "      <td>110.618657</td>\n",
       "      <td>229.993587</td>\n",
       "      <td>161.071962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.855627</td>\n",
       "      <td>275.434131</td>\n",
       "      <td>115.129859</td>\n",
       "      <td>166.354393</td>\n",
       "      <td>138.409133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.220657</td>\n",
       "      <td>150.391667</td>\n",
       "      <td>114.773099</td>\n",
       "      <td>126.426221</td>\n",
       "      <td>119.964789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.117647</td>\n",
       "      <td>126.889779</td>\n",
       "      <td>112.079338</td>\n",
       "      <td>116.641838</td>\n",
       "      <td>113.735294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.205128</td>\n",
       "      <td>131.226410</td>\n",
       "      <td>99.719744</td>\n",
       "      <td>113.672564</td>\n",
       "      <td>106.179487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.250000</td>\n",
       "      <td>132.941875</td>\n",
       "      <td>94.404375</td>\n",
       "      <td>112.275000</td>\n",
       "      <td>102.812500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>115.205556</td>\n",
       "      <td>115.205556</td>\n",
       "      <td>115.205556</td>\n",
       "      <td>114.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>122.988182</td>\n",
       "      <td>122.988182</td>\n",
       "      <td>122.988182</td>\n",
       "      <td>122.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.040000</td>\n",
       "      <td>108.360400</td>\n",
       "      <td>104.990000</td>\n",
       "      <td>106.236800</td>\n",
       "      <td>105.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.197917</td>\n",
       "      <td>140.265521</td>\n",
       "      <td>109.135208</td>\n",
       "      <td>118.319375</td>\n",
       "      <td>112.979167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.349439</td>\n",
       "      <td>186.742623</td>\n",
       "      <td>120.852770</td>\n",
       "      <td>142.838171</td>\n",
       "      <td>131.246764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.230290</td>\n",
       "      <td>338.310458</td>\n",
       "      <td>116.919407</td>\n",
       "      <td>174.352559</td>\n",
       "      <td>142.871369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.293701</td>\n",
       "      <td>678.539728</td>\n",
       "      <td>105.700790</td>\n",
       "      <td>220.163139</td>\n",
       "      <td>152.585679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.668979</td>\n",
       "      <td>1080.756289</td>\n",
       "      <td>97.765803</td>\n",
       "      <td>258.496134</td>\n",
       "      <td>159.127001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8.165624</td>\n",
       "      <td>1348.017660</td>\n",
       "      <td>94.343851</td>\n",
       "      <td>281.445832</td>\n",
       "      <td>163.001656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.777024</td>\n",
       "      <td>1459.336816</td>\n",
       "      <td>93.372997</td>\n",
       "      <td>288.514412</td>\n",
       "      <td>164.250282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8.363968</td>\n",
       "      <td>1401.566165</td>\n",
       "      <td>94.698203</td>\n",
       "      <td>286.396445</td>\n",
       "      <td>165.223304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.822950</td>\n",
       "      <td>1136.324212</td>\n",
       "      <td>97.445085</td>\n",
       "      <td>270.045156</td>\n",
       "      <td>163.343069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.815778</td>\n",
       "      <td>1133.852732</td>\n",
       "      <td>98.212157</td>\n",
       "      <td>268.747901</td>\n",
       "      <td>163.426483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8.538012</td>\n",
       "      <td>1443.254161</td>\n",
       "      <td>95.132827</td>\n",
       "      <td>289.873504</td>\n",
       "      <td>167.120897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10.030233</td>\n",
       "      <td>1725.609263</td>\n",
       "      <td>93.423413</td>\n",
       "      <td>307.619848</td>\n",
       "      <td>170.607074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10.236193</td>\n",
       "      <td>1782.070786</td>\n",
       "      <td>93.736839</td>\n",
       "      <td>313.177340</td>\n",
       "      <td>172.460842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8.900090</td>\n",
       "      <td>1558.505424</td>\n",
       "      <td>95.645219</td>\n",
       "      <td>307.019893</td>\n",
       "      <td>173.070258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.157325</td>\n",
       "      <td>1073.311806</td>\n",
       "      <td>101.225084</td>\n",
       "      <td>278.565499</td>\n",
       "      <td>169.806362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count  res_time_sum  res_time_min  res_time_max  res_time_avg\n",
       "created_at                                                                   \n",
       "0            3.506676    595.017262    110.618657    229.993587    161.071962\n",
       "1            1.855627    275.434131    115.129859    166.354393    138.409133\n",
       "2            1.220657    150.391667    114.773099    126.426221    119.964789\n",
       "3            1.117647    126.889779    112.079338    116.641838    113.735294\n",
       "4            1.205128    131.226410     99.719744    113.672564    106.179487\n",
       "5            1.250000    132.941875     94.404375    112.275000    102.812500\n",
       "6            1.000000    115.205556    115.205556    115.205556    114.888889\n",
       "7            1.000000    122.988182    122.988182    122.988182    122.636364\n",
       "8            1.040000    108.360400    104.990000    106.236800    105.120000\n",
       "9            1.197917    140.265521    109.135208    118.319375    112.979167\n",
       "10           1.349439    186.742623    120.852770    142.838171    131.246764\n",
       "11           2.230290    338.310458    116.919407    174.352559    142.871369\n",
       "12           4.293701    678.539728    105.700790    220.163139    152.585679\n",
       "13           6.668979   1080.756289     97.765803    258.496134    159.127001\n",
       "14           8.165624   1348.017660     94.343851    281.445832    163.001656\n",
       "15           8.777024   1459.336816     93.372997    288.514412    164.250282\n",
       "16           8.363968   1401.566165     94.698203    286.396445    165.223304\n",
       "17           6.822950   1136.324212     97.445085    270.045156    163.343069\n",
       "18           6.815778   1133.852732     98.212157    268.747901    163.426483\n",
       "19           8.538012   1443.254161     95.132827    289.873504    167.120897\n",
       "20          10.030233   1725.609263     93.423413    307.619848    170.607074\n",
       "21          10.236193   1782.070786     93.736839    313.177340    172.460842\n",
       "22           8.900090   1558.505424     95.645219    307.019893    173.070258\n",
       "23           6.157325   1073.311806    101.225084    278.565499    169.806362"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = df4[['count','res_time_sum','res_time_min','res_time_max','res_time_avg']].groupby(by=df4.index.hour).mean()\n",
    "df5.head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df5.index\n",
    "y = df5[['count']]\n",
    "plt.plot(x,y)\n",
    "plt.title('一天中api调用次数')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RcuTIkVJKKdesWSPT09OrPN/ChQvlwoULr9nev3//sp87dOggs7OzZUBAgDx16pR88MEH5XPPPVd2XI8ePWRKSkql579yTP/+/eWWLVvkzJkz5ebNm2VMTIxMSUmRYWFhUkopCwsLZZ8+feT48ePl7t27Kz1Xffj9KzepKE/KvV9K+UVfKRfeKeWaZ6Tc+bGUR9dKmXBEe98cHVou5Qw3KbfONXUkNy8vTcrZ/lL+OMEgpwMiZA1ybP0sxZhIZbM77t69m23btpGRkcGlS5cYOnRo2eyO7dq1Y+jQobW6ZlWzO+7Zs4fvv/++bHbHyqbtrWx2Ryklr7zyCk2aNCn7a8LOzo5Ro0axbNkywsPDaxWvUo8UZEHEt9pIx9xkbV7wkkI49ivkpVy9r5sfeLbSVhDybA2ebbSfPQLA1rHS05tUZhz8+gL43QJ9XzR1NDfP0QN6PwN/vqPdAPa/pU4uqxJ7OZXN7tijRw8eeugh1q9ff9XsjrNnz+bee+9lx44d15RarnB0dCQ1NRXQ/jISNbyTHxgYyKhRoxg4cCCLFy++odkdZ82axdKlS7G2tmbw4MEAZGVlsXLlSrp27cq6desYOXJkjc+n1EO5qbDvc20mwYJMaHM79HsJWvb6Z5/8DEg7888j9bT2HLsO8lLLnUxAx1Fw9zf1Y1g+aIOQVj+pLTc39iuwNvM01fNJ2PuFNgvlA2vr5JJm/hszrkcffZSHHnqIhQsX4ubmxk8//YROp+PVV1/l3XffxcHBodrZHcePH8+PP/7InDlz6NevX42u+9prr/HII48wffp0WrVqdc08MdczduxYhg8fjp+fH8XFxRQUFPDvf/+b559/nkGDBnHHHXcwcODAa77EFDOQdRl2L9CWYivO1+Yl6fuCNkdJRY6NtOXafK+ZtunqpB+3H/Z9obXaR39eP7oR7vsczm6HkZ9of12YO3sX7b/T7//WPlermuWB2qjx7I6GFBYWJiMiIq7apmYXNC31+6/H0s7ArvkQ9RPoSrUh672ngXcHw5x/23taz43ez8GgWYY5581KPKLNkNh2kLZmaX34ojGE4gL4pBu4+8Ejm276cxl0dkfl+tTsjopRJB6FnR/B4RVgZQvdpkDvZ7W6uCH1fwVyErQvD5dmEP6UYc9fUyWFsPJRcGgEd31iOUkdwNYB+r8M65+Hk5ug/RCjXk4ldgNQszsqBpV2FjZN1+ZEsXWG8KkQ/jS4NjPO9YTQRnPmJsPvr4OLN3QZZ5xrXc+fb2uTe937Mzhb4Cqc3aZoX55/vq39RWJlvN7mqh+7otQnpcWwbIpWi+3/Gjx/WJv+1VhJ/Qoraxj7jTZk/5cn4PSfxr1eRWd3aPcPwh42emvWZKxtYcDrWp/2WMP9RV8ZldgVpT7Z/QkkxsDoz2Dg63U70tLWQatrewVqXy6XI+vmuvkZ2peJZ2uTzmFeJ7rcA14dYOts7X6JkajErij1Rcop7UZm0F1ajxdTcGwEk1eAoycsHqd1kzS2316C7HgY+zXYWfii61bW2rJ5KSfg0DLjXcZoZ1YUpeZ0Olj3rNZqHm66ebwBcGsOU1aB1MGiMZCdaLxrxayAmOXaOqF+3Y13nfok6C7oNEb78jQSldiNSM3uqNTYwe/h/C4Y/C64NjV1NNCkndZyz02GH+/WRrkamqWMLr1RQmiTggXWbtT69dTLXjHv/f0ex9KOGfScHTw78GqPVw16zuqo2R2VGsm6DJtnaANXut1n6mj+4dcdxi+CJRNg2WQt0dvYG+bclja6tJ5RLfZyzG12x8uXL9OnTx/69u3LG2+8AWijZa98oTz++OPs37+fxMRE+vXrR48ePZgyZQrffPPNTf+OFAOTEn59UesNM3J+/eu73e4OGPWp1kvnl8e1hGwIV0aXDp1jGaNL65uazBRm6Iea3bF/2c+1md3x77//ljt27JDZ2dmyS5cuUkopN27cKGfPni2llLJfv35SSilXrVol33nnHRkXFydHjBhR6bnqw++/QYpZqc1guHO+qSO5vp3ztTh/fVlKna5250o4LOUsLyl/mlj7czUwqNkdb5y5ze5oY2PDW2+9hYuLC9nZ2QDcfvvtfP7558TGxpbNY9O6dWtmzZrFhg0bmDlzZq3iVQwoLw02vALNQ+BWE432rKnez0JOIuxZoN0DuNmaeNnoUjdtLpj69heKhVCJvRxzm93xww8/5PXXXyc4OJiuXbsCWrL38vJiyZIljBunjR5cs2YN3333Hd26VTJZlGI6m6ZDfjpM+cU8asyD3oacJPhjljYdbdeJYOd0Y+coP7rUxcs4cSoqsV9PfZ/dccSIETzxxBN4eXnh5OTEpUuX8PX1ZcyYMUybNo233noL0Or4d999N61atcLX15c5c+bg6+t7478QxXBO/wlRP2ot32ZdTB1NzVhZafX2vBRtzpP1z4Odizb839kbnL20ZO1c4eGify/xsDa6tPtDlju6tJ5Qszs2ADNnzmTXrl1YW1tjY2PDe++9R6dOna7aR/3+61BRLnx2K1jbwxM7tb7r5qQoD2LXar15cpO1R04S5KZAbpI237us4iarZxt4YoflD0QyEjW7Yx2q77M7qrp6PfPnu5BxAR7aYH5JHbTyS/B11gjQlWr3D3KTtUSfm6Il/oIM6DpBJfU6UK8S+43UoesTc5/d0RR/tTVYcQe0rn5hD1+94pElsbLWSjIuXkBHU0fTINWbfuwODg6kpqaqJFPHpJSkpqbi4GCGLUdzU1IEa5/W5jy/4y1TR6NYsHrTYvfz8yMuLo7k5GRTh9LgODg44OfnZ+owLN+ujyHpKExaqnX3UxQjqTeJ3dbWllatWpk6DEUxjuTjsH0edBoLgcNMHY1i4epNKUZRLJZOB2uf1W4aDvuPqaNRGoB602JXFIsV8S1c3Aujv1CDcpQ6oVrsimJMGRdhy0xoc9v1uwgqigGpxK4oxvTby9pgnREfq3lRlDqjEruiGEvycTixAfq+AB4tTR2N0oCoxK4oxhK5GKxsIPQBU0eiNDAqsSuKMZQWQ/QSaD9UmwRLUeqQSuyKYgwnftfmSuk2xdSRKA2QSuyKYgyRi7SpA9reYepIlAZIJXZFMbSseDi5CUImmccCGorFqTaxCyHchRAbhBCbhBC/CCHshBAXhBDb9I8u+v3eEkLsF0J8avywFaUei16idXFUZRjFRGrSYp8MfCilHAwkAK8BS6SUA/SPGCFEd6AP0ANIEkKovz+VhklKrTdMi17QuI2po1EaqGoTu5TyMynlZv1LL6AEGCGE+FsI8a0QwgboD6zUr6L9O9C34nmEEI8JISKEEBFqBkfFYl3YA2mnIVS11hXTqXGNXQgRDngAm4E7pJQ9AFtgOOAMXNLvmgY0rXi8lPIrKWWYlDLMy0vNl6FYqIOLwM4VOo4ydSRKA1ajOztCCE/gv8DdQIKUslD/VgTQDsgBHPXbXFA3ZZWGqCALjq6GruPV8m+KSdXk5qkdsBx4XUp5HlgkhAgWQlgDo4Fo4ABajR0gGDhnnHAVpR47sgqK89RNU8XkatKyfgQIBd4QQmwDjgCLgChgj5RyC7AT6CaEmI/+5qpxwlWUeuzgIvAKAt/upo5EaeCqLcVIKT8HPq+w+a0K++j0PWHuBOZLKc8aLkRFMQNJsXApAobMVrM4KiZnsNETUsp8YIWhzqcoZuXgIrCyha4TTB2JoqibnIpSayVFcGiptpapcxNTR6MoKrErSq2d2AB5qRB6v6kjURRAJXZFqb2Di8DVR1v+TlHqAZXYFaU2Mi/B6T8g5F6wsjZ1NIoCqMSuKLUT/ZN+wq/7TB2JopRRiV1RbpZOp034FdAXPFuZOhpFKaMSu6LcrPM7If2cGmmq1DsqsSvKzYpcDPbu0PEuU0eiKFdRiV1RbkZ+BhxdA13Gga1j9fsrSh1SiV1RbsbhFVBSoOZdV+ol80jsuanweW+tv7Ci1AeRi6FpZ2geYupIFOUa5pHYnTwhOwHO7zZ1JIoCCYfhcqR201RN+KXUQ+aR2IUA/55wca+pI1EUiFwE1nbaghqKUg+ZR2IHaHErpJ2BnCRTR6I0ZCWFcGgZdLhT+0tSUeoh80rsABdUq10xoWO/Qn666ruu1Gvmk9ibB4ONg0rsimlFLgJ3f2g9wNSRKEqVzCex29iDT6iqsyumk3EBTm9VE34p9Z75JHbQyjHx0VCUZ+pIlIYoSr+Ub8hk08ahKNUwv8SuK4FLB0wdidLQ6HQQtRha9wePlqaORlGuy7wSu38P7VmVY5S6dm6HVopRN00VM2Beid3RA7yC1A1Upe4dXQ22zlo3R0Wp58wrsYNWjrm4H3Slpo5EaSh0pRC7HtoNUhN+KWbBPBN7YSYkxZo6EqWhuPg35Cap6XkVs2GeiR1UnV2pO7Frwdoe2g02dSSKUiPml9gbtQSXZqrOrtQNKSF2HbS5DexdTR2NotSI+SV2IaBFT7iwz9SRKA3B5UjIvAhBI00diaLUmPkldoAW4ZB5ATIvmToSxdLFrgUrGwgcZupIFKXGzDOx+/fUnlWdXTEmKeHoWgjoq2ZyVMyKeSb2Zl21PsWqzq4YU1IspJ1WZRjF7JhnYre2Ab/uKrErxhW7FhDQYYSpI1GUG2KeiR20OnviYSjMNnUkiqWKXad1r3VtaupIFOWGmG9i9+8JUgdx+00diWKJUk9rDYcgNShJMT/mm9j9bgFhpbo9KsYRu1Z7VvV1xQyZb2J3cIOmneDCHlNHolii2HXg0w0a+Zs6EkW5Yeab2EGrs8dFQGmJqSNRLElmnDbnvyrDKGbKvBO7f08ozoXEGFNHoliS2HXas0rsipmqNrELIdyFEBuEEJuEEL8IIeyEEN8KIfYIIaaX2++abUbXIlx7VnV2xZBi14F3R2jS1tSRKMpNqUmLfTLwoZRyMJAATASspZThQGshRDshxNiK24wXcjnuvtqK8arOrhhKThKc361a64pZqzaxSyk/k1Ju1r/0Au4Dfta/3gT0AQZUsu0qQojHhBARQoiI5OTk2sb9D/+ecHGfNvxbUWrr2K+AVL1hFLNW4xq7ECIc8AAuAldm30oDmgLOlWy7ipTyKyllmJQyzMvLq1ZBX6XFrZAdDxnnDXdOpeGKXQuerbUeV4pipmqU2IUQnsB/gYeBHODK+mAu+nNUtq1uXFl4Q9XZldrKT4ez27UyjBCmjkZRblpNbp7aAcuB16WU54ED/FNqCQbOVbGtbnh3BHs3NdOjUnvHN4KuRNXXFbNnU4N9HgFCgTeEEG8AC4EpQggfYBhwKyCBHRW21Q0ra20UqpoQTKmt2LXg5gu+oaaORFFqpSY3Tz+XUnpIKQfoHz+g3SzdCwyUUmZKKbMqbjNm0NdoEa5NsZqfXqeXVSxIYQ6c+kO7aarKMIqZu6lauJQyXUr5s5Qy4Xrb6kyLnoCEi2pCMOUmndwEpYWqDKNYBPMeeXqFb3dt+TJVZ1duVuxacPb652a8opgxy0jsds7aqkqqzq7cjOICOLEJOtyp3bNRFDNnGYkdtDr7pQNQUmTqSBRzc/pPbc4hVYZRLIQFJfaeUFIA8dGmjkQxN7FrwcFdW7RaUSyA5SR2f31tVNXZlRtRWgzHf4PA4WBjZ+poFMUgLCexuzYFj1aqzq7cmLPboSBTlWEUi2I5iR20Hg0X9qoJwZSai10Hts7QZqCpI1EUg7G8xJ6Xoi1ErCjV0ZXCsfXQfjDYOla/v6KYCctK7KrOrtyIC3shN1lN0atYHMtK7E3ag6OHqrMrNRO7Dqztod1gU0eiKAZlWYndykpbeEMldqU6UmqJve3tYO9q6mgUxaAsK7GDVmdPPQm5KaaORKnPLh+ErDhVhlEskuUl9rI6u1p4Q7mOo2u1+YXaDzV1JIpicJaX2H26gbWdKscoVZNSG20a0BecPE0djaIYnOUldlsHLbmrxK5UJekopJ2BjmpQkmKZLC+xg1ZnvxwJxfmmjkSpj46uBQR0GGHqSBTFKGqyNJ758b8Vds3XknvLXqaORqlPSosh6icI6AMu3qaORjETUkoKSwspKCkgvySf/NJ88kvyKSgpQCAI9AzE2dbZ1GGWsdDE3lN7vrBXJXblaodXQeYFGP4fU0eimFBecR6Xci5xOedstuuvAAAgAElEQVRy2fPl3MtkFGaQX5xPQak+gZf8k8AlVU9VYiWsaNuoLcFewXT16kqwVzAt3VpiJUxTFLHMxO7cWBuspOrsSnk6Hez8CLyCoN0QU0ejGFFecV5Zsr4mgedcJr3w6vWR7a3t8XHxwcPeA3cHd5pZN8PRxhFHG0ccbByuei7bbu2Ao60jRaVFHE45THRyNBvPbmT5ieUAuNm50dWra1mi79KkC652dTNmwjITO2it9th12v/MVpZ5K0G5QSc3QXIsjPlS/ZuwMIm5iRxMOsjBxINEJkVyIv3EVS1sOys7fFx88HXxpWPjjvi4+ODn4oePiw8+Lj40dmiMqMUi5v38+gGgkzrOZp7lUPIhopOjiU6OZtelXUgkAkGbRm3KEn2PZj3wc/Wr9WevjJAmmAkxLCxMRkREGPcikT/CmqfgqX3g3cG411LMw7dDIOsSPBsJ1ramjka5SVJKzmSe4WDSQSITIzmYdJBLOZcAcLRxJNgrmG7e3QhwC8DX1RdfF188HTxNVhbJLsoua9EfSj7EoZRDZBZmMjVkKk8EP3FD5xJCHJBShlW3n+W22K8sSnxhj0rsCpzfo00ON+w/KqmbmeLSYo6mHSUyMZIDSQeISooiozADAE8HT7o37c7koMmENg0l0CMQG6v6ldZc7VwJ9wkn3Ccc0L6Yzmedx8nWyWjXrF+/AUPybK2tOn9xH4Q9ZOpoFFPb+RE4NYZuU0wdiVKNotIiYlJi2J+wn4iECKKToykoLQCgpVtLBvgPINQ7lNCmobRwbVGrEoopCCEIcA8w6jUsN7ELobXaz25XdfaGLuEwnPwdBk4HO+O1ksxJqa6UnOIcsoqysLOyw8vJy2SliqLSIg4lH2J/4j+JvLC0EIGgg2cHxrUfR2jTULp5d6OJYxOTxGhuLDexA3Qcrd1APb8TWvUzdTSKqeyaD3Yu0ONfpo7E4IpKi0grSCO9IJ30gnQyizLJKswiq+ifR3ZR9tXbCrPIKc656uairZVt2c3FK89XHoa4uVgx5usl8vGB47ml6S2ENg3F3d7dINdsaCw7sXe4E+zdIGqJSuwNVfo5OLwSbn1Sm6vfDBSWFnIu8xxpBWllSfvKzxVf5xTnVHkee2t73OzctIe9G95O3rRt1BZXO1fc7N3K3issLSQuJ07rEph9idjU2Gu6AzpYO1yV+Js6NUUIgZQSiUQnddoXhQQdOqTUtgFl75XoSjiWdkwl8jpg2Ynd1hE6jYaYlTB8Hti7mDoipa7tXgDCCsKnmjqSKpXqSolNi2Vv/F72xe8jMimSwtLCq/axFtY0sm+Ep6MnnvaedGrcCU9HTzzsPcq2eTh44G7vXpbI7a3tbzqm8gN44nLiruoLHpUcRXZRdrXnsBJWCARCCAQCK2FFa/fWKpHXAbNI7DqpY8WJFfTz60cz52Y3dnDwJDj4P60kEzLJOAEq9VNOMkQuguCJ4OZj6mjKSCk5m3m2LJHvT9xflijbNmrLPe3vIdgrmCaOTfB08MTTwRM3e7c6rYE72TrRzqMd7TzaVfp+QYl2M1MIgRVWIMAKq7Ikbm43NC2NWST2pLwk3o94n20Xt/Hp7Z/e2D+aFuHgEQDRS1Rib2j2fQElhdD7OVNHQkJuAvvi95U9kvKTAPB18WVQy0H0bNaTHs17mM3NQQcbB1OHoFyHWST2Zs7NeC70Oeb+PZf1Z9Yzss0NrHojhNZq3zYXMuPA3TgjvZR6piAL9n+trZDUpPJWp7FdyLrA0uNL2RG3g3NZ5wCt33WPZj3o2bwnPZv3xN/V3ySxKZbNLBI7wKQOk9h4diPv7X+PcJ/wG2vZdJ0A2+ZA9FLo95LxglTqjwMLoSAT+kyr08tKKYlIjOB/R//HXxf/wtrKmvDm4dzT/h56Nu9JO492JutWqDQcZjWlwJnMM9yz9h4G+A/ggwEf3NjB3w2D3CR4OkJrxSuWq7gA5ncFrw7wwNq6uWRpMRvPbWTR0UXEpsXSyL4R4wPHMzFwIl5OXnUSg2L5LHJKgdburXky5EnmH5zPH+f/4PaWt9f84JBJsPYZuHQA/Kr9vSjm7NBSyEnUJvsysoyCDJafWM6SY0tIzk+mtXtrZoTPYETrEaoOrZiMWSV2gAc6PcCmc5t4Z987hDULq3l3qY6j4bdXtEUWVGK3XLpSbUBS8xBoPcBolzmTeYbFRxez7vQ6CkoL6OXTi1m9Z9HLp5cqtSgmZ3b/Am2tbJnVexYZBRnM2z+v5gc6uEHQCG2wSklh9fsr5il2rbaeaZ/nDV5yk1Ky5/IentryFKNWj2LNqTUMbz2cVXet4stBX9LHt49K6kq9YHYtdoAOnh14qPNDfB3zNcNaDaO3b++aHRg8CWKWw/EN2sAlxbJIqU325dlG6w1jQHvj9zJv/zxOpJ/A08GTp0KeYnz78TR2bGzQ6yiKIdSoeSGEaCqE2KH/2VcIESeE2KZ/eOm3fyuE2COEmG7MgK94PPhxWrm34q09b5FbnFuzg1oPANfmWp92xfKc2Qrx0Vq/dStrg5wyuyibmbtn8uimRykoKWBWr1lsGreJJ4OfVEldqbeqTexCCA/gB+DKSq09gXellAP0j2QhxFjAWkoZDrQWQhi947C9tT2zes0iITeBjw58VLODrKyh63g4uVkblahYlp0faV/cwRMNcrrtcdsZvWY0v5z6hYc6PcTKu1Yypt2YWg3VV5S6UJMWeykwAcjSv74V+JcQ4qAQYrZ+2wDgZ/3Pm4A+FU8ihHhMCBEhhIhITjZMUg3xDmFy0GSWHV9GREINu08G3wuyVCvJKJYj7oA2RXP4VLCpXeLNLMzkjZ1vMPWPqbjZubF42GJeCHtB9XJRzEa1iV1KmSWlzCy3aQNaIr8FCBdCdEVrzV/Sv58GNK3kPF9JKcOklGFeXobr1/tMt2fwdfFl5p6ZZfNXXJd3B/DpBtE/GSwGpR7Y9RE4uEP3B2t1mj/O/8Go1aP49cyvPNb1MZaNWEYXry6GiVFR6sjN3MLfLaXMllKWApFAOyAHcNS/73KT570pTrZOzOw1k/NZ5/ks6rOaHRQ8CRJitAUYFPOXfAJi10OPx8D+5laBTytI46W/XmLatml4OXmx5M4lPNPtGeys7QwcrKIY380k4N+FEM2FEE7AYOAwcIB/yi/BwDnDhFcztza/lbvb3c0PR3/gcEoNknXncWBlq26iWord88HGAXre2MLAoHVh3HB2A6NXj+aPC3/wTLdn+OnOnwhqHGSEQBWlbtxMYn8L2ArsBb6QUh4HVgNThBAfAuOBXw0XYs28GPYiTRya8H+7/4/i0uLr7+zcGNoPgUM/Q2lJ3QSoGEfmJYheBqFTwPnGZkZMzkvmua3P8cr2V/B18WX5iOU81vUxbK3UYteKeatxYpdSDtA/b5VSdpBSdpVSLtBvy0Kru+8FBlaoydcJVztX3gx/k5PpJ/km5pvqDwiepM0dc/pP4wenGM+eT0HqIPzpGh8ipWTNqTWMWjOK3Zd382L3F1k0fBFtPdoaMVBFqTsGq4VLKdOllD9LKRMMdc4bNcB/AMNaDeOrmK84mX7y+ju3GwyOnuomqjnLSYYD30OXceDRskaHZBVl8ezWZ5m+azrtGrVjxcgVPNj5QWyszHKsnqJUyuLGP7/W4zVcbV35v13/R4nuOmUWGzstIRz7DfLTq95Pqb82/x+UFkG/l2u0+4n0E0xaP4mdcTt5KewlFg5dSIB7gHFjVBQTsLjE7ungyes9X+dw6mEWH118/Z2DJ0FpIRz5pW6CUwzn/B7tr61eT9doIY3fzvzGfb/dR15JHt8N/Y4HOj2g5nVRLJZF/sseGjCUAf4DWBC1gPNZ56ve0aebNmd39NK6C06pvdIS+O0lcPOrtrVerCvmvb/f49UdrxLkGcTPI36mm3e3OgpUUUzDIhO7EII3b30TOys73tz1JsW6KnrJCKENP7+4D1JP122Qys3b/zUkHoahs8HOucrdUvJTeHTToyyOXczkoMl8M+QbteiF0iBYZGIH8Hby5o1b3yAyKZIPIq6z2lLXCSCsVJ92c5GdAFtnQ5vbIeiuKneLSopiwroJHEk5wpy+c3itx2uqG6PSYFhsYge4s/Wd3Bd0Hz/G/sja01Uskebmo836GL0UdLq6DE+5GZv/D0oKYPi8Sudbl1Ky7NgyHvr9Ieys7Vg8fDEjWo8wQaCKYjoWndhBG7jUo1kP3tr9FkdSjlS+U/C9kHkRzu+s2+CUG3NuJxxaBr2ehcZtrnm7oKSA6bum886+dwhvHs7SEUsJ9Aw0QaCKYloWn9htrGyY138ejR0b89zW50jJT7l2pw53gp2ruolan5UWw68vgXsL6PviNW/HZcdx/4b7WXt6LU8FP8WC2xfUfNlERbEwFp/YQesC+fHAj8kozOClv1669maqnRN0GgVH10BRDRftUOrWvi8hORaGzdX+e5Wz69IuJqyfQFxOHJ/e/ilPhjypujIqDVqD+dffsXFHZvaayYHEA5WvlRp8LxTlQOy6ug9Oub6sy7BtDrQbAoHDyzZLKfn60Nc8ueVJmjo3Zdmdy+jn18+EgSpK/dBgEjvAiNYjmNJxCkuOLWH1qdVXv9kiHBq1hCg1xUC9s2m6VooZNveqG6aLYxfzSeQnDG01lMXDFuPv5m/CIBWl/mhQiR3ghe4v0LNZT97e8zYxyTH/vGFlpfVpP7sdMuNMF6BytTN/weGV0Od58Gxdtnlv/F4+iPiA2/xvY27fuTjZOl3nJIrSsDS4xH7lZmoTxyZM2zbt6pupwRMBqfW8UEyvpAh+exk8AqDPtLLNF7Mv8tJfL9HKvRWz+85W9XRFqaBB/h/h4eDB/Nvmk1WYxYvbXvxn/nbP1lpJJmoJSGnaIBXY+xmkHIdh/wFbbYGuvOI8ntv6HDqpY/7A+TjbVj3yVFEaqgaZ2AE6eHbgrV5vcTDpIO/tf++fN4InQepJ+PNtrcWomEZmHPz1H+1mafshgHazdPqu6ZzOOM37/d6nhVsLEwepKPVTg03sAMNbD+eBjg+w7Pgyfjmpn+ExeKI2zcCOD+CrAXA50qQxNli/vwGyFIbOLdv0dczXbD6/mRe6v0Av314mDE5R6rcGndgBpnWfRs/mPXl779scSj4ENvYw9iuYtBTyUuHr2+GPWVBSaOpQG45Tf8DR1dD3pbIFNP66+BcLIhcwovUI7u94v4kDVJT6rcEndhsrG97v9z7eTt48v/X5f26mBg6DqXu1FvyOD+DL/nDpgGmDrUM5Weks/+4JZs/vSszBTXV34ZJC2PCKdr+j1zMAnMk4o0272ziIGeEzEJXMEaMoyj8afGIHaOTQiPkD55NVlMUL217452aqoweM/gwmr4CCTPjmDtg8A4oLTBuwERXk57Jm8cu8uehWZlnvYkkjyfx9z5J0IbZuAtizAFJPwbB5YOtQtpSdvbU98wfOx8HGoW7iUBQzphK7XqBnILN6zyIyKZK5f89Flu8V026Q1noPmQy7PoYv+8LF/aYL1ghKiovYuPwdZnwTxoySDexwsmOs12Ae8J3IPidb/rvmHoqyKplnx5AyLsBf8yBoJLS7g1JdKa9uf5VL2Zf4cMCHNHNudtXu244nMWDeVu75YjcfbzlBxLk0ikvVDJ2KolbwLWdYq2HEpsay8MhCYlJimNRhEsNaDdNaiQ7uMGoBdBoNa5+D7wbDrU/BbdPLuuKZI11pKX9t+Iw/z3zBb64CnasNgxqF8+qQ2TRxbAJA3MqLrGYXzX8YxpOP/4WwM9JgoI2vayNLh8wB4L+R/2XnpZ28eeubdG/a/Z+YdZLPtp3ig80naOPlQmGJjvl/nOTjLSdxsbfh1tae9G7bhD5tm9DW20WVbpQGR0gT9NcOCwuTERERdX7dmijVlbLq1Cp+iv2JUxmncLd3Z2zbsYwPHI+fq5+2U0GWNi/4gYXQuC2M+hRa3GrawG+Q1OnYu/UnthyZx3rXEgqEFb2duvDG0P/g6+Z31b6lulImLL6LU7rzzCjwZcxjv4GVtWEDOrwKVjwEt8+Avi+w8exGXt7+MuPaj2NG+Iyy3bILinnx52g2HU1kVIgPc8d2xdHOmoy8IvacTmXnqRR2nUrhXGoeAN6u9vRp24Te+kczd1XKUcyXEOKAlDKs2v1UYq+clJKIxAiWHFvCnxf+RCd19Pfrz8QOEwn3CddGO57ZBmufgYyL0PMJGPi61rKv56L3rGfD3zNZ75pHprU1YbZteGPIPNo2rnpR6OzCHEb/OJh8MvnAIZzwe78xTDBSauWtLW9pa9A+/DvHss4w5bcpBDUO4tvB32Jrra18dCoph8cXRXAuNY9/Dw/i4d4BVbbGL6blsetUCjtPpbD7dCppudqYhLbeLvRp24TR3XwJ8W9kmM+gKHVEJXYDSshNYPmJ5aw4sYK0gjRaurVkYuBERrUdhasUsGWmtg4nQmvB+3QDnxDtuVlXsHcx9UcA4Fj0Tn7961V+dU4j2caGzlY+vHbHewQ3D6nR8SfTLnD/6lE00uXzqf8DtB7yeu0CKs7XvhhjlkOnsTDqU9J0BUxaP4kSWcKyEcvKykG/H0ngxZ+jsbexYsG9oYS3aVzjy+h0ktiELHadSmHHyRT2n0ujqETHi4MDebJ/G6ysVKlGMQ8NOrHnFJaw40Qym2MTOZ+ah621wNbaCjtrK+3Z5sqztv2qbdYCB1tr2ni5ENTcjaZu9mWtwqLSIjad38SSY0s4lHwIRxtHRrYeycQOE2mXl6X1v74cpQ1qyr6sj0ZAk3Zakm9+Jdl3MX6y15VqvUvio+FyFCtObuVbx3TibG1piycv9X+b3gE3PsXtppP7eHXnv+haVMD8sFk06j7p5uLLvARL79Xiu2069H2RYlnC45sf51DyIX4Y+gOdmnSiVCf5cPNxPt16mmA/dz6/rzs+jWp3TyOnsITXV8WwLvoyAwK9+Gh8CB7OdrU6p6LUhQaX2BMyC9gSm8jmo4nsOZ1KUakOd0dbOjZ3o1RKikp0FJdeeVz9WvtZUlRJjwoPJ1uCmrvRoZkbQc1dCWruRltvF05nHmPJsSVsOLuBIl0RtzS7hQc6PkA/v37aF0F2IsRHaYk+/kqyj9efVYBXoJbom3UG1+bg4g0uTbWHg3ul63lWSVcKKSf014rWrhd/CIpz0QGfeXrypbsLzUqdeTF8OkM63FmrG4r/3fUTX52aw8jsPGYN/x6b1n1v7AQX/4alk6E4D+7+RhszAMzeN5slx5Ywu89sRrYZSWZeMc8ujeSvE8lMCPPnrVGdcLA1TG1fSsnifRd4e91RmrjYsWByKKEtPAxybkUxFotP7FJKjsZnseVoEltiE4m5lAlAy8ZODApqyh0dmxLW0gMb65r36JRSUqKT5BaWcCIxh9j4LI4lZHE0PpvjCVkUFGuJ39pK0MbLmaDmbrT0gjSrHexNWU9iXjxdmnRhashUevn0ujZ5ZidcnegvR0FOwrWBWNvrk7z3P8+uzf557egJaWf0CTwaEmK0JAlg46j9ReATQp53R15K+IsdqQdwLLyVzffPx93BMD1apq6ZyfaMlUzNyOOJSb+Cd4eaHRi5GNY/D26+MGkJeAeRWZjJl4e+ZNHRRdzf8X5evuVlYuOzeHzRAeIz85l5Vyfu7dHCKL1bYuIyeeqnA8RnFPB6NXV7RTE1i0zsRSU69p1NZfPRRLYcTeRyZgFCQDf/Rgzq2IxBHb1p42Wc7m2lOsn51Fxi47PLEn5sfDaXMvKv7EHnwBMUuPxOYl4C3by78XTI0/Ro3uP6J85Ph5wkyEn85zk74dpteZX0Ibd1huZdtZZ/82Ctrt+kPVhZE5cdx7N/PsvJ9FOUpIxg1eRXCWpuuBu7UkpGLX6As7pI5qaXcOfDf4Jb86oPKC2BzW9qMza2HgDjFpJn68BPx37iu5jvyCnOYWy7sUy/dTq/xSTx6opDuDna8Nnk7nRvadyWdGZeMS+tiGbz0USGdmrGf+7pipuDrVGvqSg3w6ISe1puEW+uOcxfx5PJKSzBwdaKvu28GBTUlIEdvPFytTditNeXmVfMsYQs9p1N45M/TtKisS1397/MqjP/Iyk/iR7NejA1ZCqhTUNrd6HSYshN1pJ8bio08tdu1FbS7fDv+L958a8XyS8uJv3cBN4YOJqH+7Sq3fUrkVOUz7DFo8kXl/gq14nQR/8Ae9drd8xLgxUPw5mt0PNJiu+YwarTa/ni0Bek5KcwwG8Az4Q+Q2u3tszdcIxvdp7llgAPPp0cirdr3XRPlFLy7c6zzN1wDJ9Gjnw2OZTOvvW/h5PSsFhUYi8p1XHnJzsJbdmIO4Ka0rttE4PVWg1p75lUnliszSfzyaTOXCj+k29iviG1IJVePr14OuRpunh1Mdr1pZQsPb6U9/5+j+ZO/pw5cg89/Tvw/YO3GK3nx6nUeCatHoMrWfxg1Qr/+1eDdbnWbtIxWDoJMi6iu/NDfvf04r+R/+Vi9kVCvUOZ1n0aIV4hnEjMYebaI+w5k8oD4S15486O2NnU/cDoA+fTePqnSFJzi5gxsqPRSkCKcjMsKrGbk/OpuTz8/X7Op+bxzujO3NWtCcuOL+O7w9+RUZhBf7/+TA2ZSlDjIINet7i0mHf3vcvKkyvp69uPM0dGkZQl2PhcX7zdjNvq3XQyild3Pki74ny+bdwP17FfaTd/j2+AlY8ibR3ZdcdrzI/byLG0Y7T3aM+jnZ+mNCeQ7SdS2H4ymfjMAuxtrJg9pgt3d/er/qJGlJZbxLRlUWw/kcyoEB9mj+mCs70apK2YnkrsJpRVUMzTP0Wy/UQyj/Rpxb+HB1FQmsePsT/y/ZHvyS7K5o4Wd/BUyFO086h6UFBNpean8sK2FziYdJB/dfkX2fF38M2Oc3x9fxiDOjY1wCeq3se71vDdyekMzMvjw8CHsLZzhD/eJsqnI/N9AohIjcHLoTmdncZzKa4DURezKNVJXB1s6NO2Cf3bezGwgzdNjfwlVFM6neTTraf4aMsJWjVx5vP7utO+aSVlJkWpQyqxm1hJqY53fo3l+93nGBjoxSeTuuHqYEt2UTaLji5i0dFF5BbnMtB/IOE+4XTz7kbbRm2xvsGh+sfSjvHsn8+SXpDOrN6zcCu9hcnf7GNyzxa8O8Z4ZZ/KPL76I3ZnfsfDGZmMzMnj4xZB/KXLwA53StJuJzMxFLChq587/dt70b+9FyH+jW6o51Jd230qhWeXRpFTWMw7o7twd6ivKs0oJqMSez3x477zzFhzhNZeznz7wC34e2rdDTMLM/n+yPesPrW6bA54Jxsnunp1pZt3N0K8Quji1QVXu6pbiRvPbeTNnW/ibu/OJ7d9QnOHtgybvwMne2t+faYvjnZ1ex9Cp9MxbPFzXJbbQILU2VOU2h/XooH0b+tH/0Av+rRtQmMX093svhlJWQU8sySSfWfT6BHgyavDOhi9p46iVEYl9npk96kUnvzxINZWgi+ndOeWAM+y96SUXM69TGRSJFFJUUQnR3Mi/QQ6qUMgaOfRjhCvEEK8tYefix8SyYLIBXwd8zUhXiF8NPAjGjs05qkfD7IlNpFfnuptsh4d2QUFjFzyIrLUgbsCJjMkqA0dm7uZ/bD9klIdS/ZfZP6Wk6TkFDK4Y1NeGRpIW29VnlHqjkrs9cyZ5Bz+9UMEF9PzmD2mC/eE+Ve5b05RDjEpMUQlRxGVFMWh5EPkFOcA0NihMU0cm3A8/Thj243ljZ5vYGdtx8/7L/LKykO8PqwDj/dvU1cfq8HJKyrhu51n+eKvM+QVlXBPd3+mDWpHc3fznbpZMR8qsddDmXnFTP3pIDtPpfB4v9a8MrQD1jVoyZbqSjmdeZqoJC3Rn8o4xei2o5nUYRJCCM6m5HLnJzsI8W/E4kd6mn3r2Byk5Rbx6dZTLNpzHiHgwV4BPDmgDY2c1JwzSvXOJOfg4mBzw+M0DJrYhRBNgRVSyr5CCFtgFeAJfCul/K6ybdc7X0NN7ADFpTpmrTvKor3nuSOoKR9PDMGlFl3pikt1jPt8N+dS89g4ra9qOdaxi2l5fLTlBL9EXsLV3oYnB7Tlod4B9XKchWJa2QXF/HoonuUH4jhwPp1nb2vLC4MDb+gcBkvsQggPYAngLaUMFUK8ALhJKWcKIX4DJgCPVtwmpcyu6pwNObFf8b8953hr3VFaNnZiyq0tGRnsQ5ObuKn4/u/HWbD1FJ9NDmV4l+sM6VeMKjY+i3m/H+fPY0k0c3Ng2h3tGNfdr173+FGMT6eT7D2TyvIDcWw4HE9BsY42Xs7cE+bP2G6+NzzGxJCJ3Q0QwBop5QAhxFrgNSnlUSHEa8A+4PmK26SUWyuc5zHgMYAWLVp0P3/+/A19IEu082QK7/4WS2x8FtZWgn7ttAUgBndsVqMeLfvOpDLx672MC/Vj3j3BdRCxUp19Z1KZu/EYkRcyaOPlzMtDOjCkU9MG0UWyqERHTmEJuYUl5JR75JY9SvFp5EiwvzvN3Bws+ndyITWPFQfjWHkgjksZ+bg62HBXsA/juvsR4t/opj+7wWvsQoht+sT+BzBWSpmpT9ZZaC32q7ZJKZdWdS7VYr/a8YRsVkddYk3kJS5nFuBsZ82Qzs0Y082XXm2aVFqHz8wvZvj8HdhYC359tm+tyjmKYUkp2XQ0kf9sPMbp5FyauNjTvWUjbgnwpHtLDzr5uJtkuoTayC4o5lhCNkcvZxEbn8WZ5FyyCorLJe7SSqe9roqXqz3Bfo0I9nOnq7/2bO73J3ILS9hwOIHlERfZdzYNIaBP2yaM6+7HkE7NDFKeq2liv5lskAM4ApmAi/51ZduUGgps5sqrQzvw8uBA/j6XxurIS/waE8+qg5fwdrXnrmAfRnfzpZOPG0IIpJRMX32YhKwCVjwRrpJ6PSOEYEinZtzewZv1h+LZfiKZiPPp/H4kEQB7GyuC/RtxS4AHYS09CW3hgbtT/ZhNUkrJpUPxiTYAAAapSURBVIx8fQLP5mh8JrHx2VxIyyvbx8PJlnbervh7OuFib4OzvTUu9ra42FvjbG+Di/7hrH+4OmjPjrbWnEvN5dDFDA7FZRIVl8GW2MSy87Zs7ERXfbIP9m9EJx83nOzq97/tguJSoi9msOJAHL/FxJNbVEpAYydeGtyesaF+tV4U5mbdTIv9/4CjUsoVQogfgC+BOypuk1LurupcqsVevYLiUrYeS2JV5CW2HU+iuFTSztuF0d18cbC15u31R3lxUHueub32UxIodSMpq4AD59PZfy6dA+fTOHI5ixKd9v9f+6YudG/pWZbs/T0djVaqkFKSW1RKSnYhKTmFnEnJJTY+q6w1nlVQAmjT/QQ0dqZjczc6+mgLzXRs7n7VqmK1lVXw/+3d329TdRjH8ffTtVu7dRsdG9DBApH4I0uAxICCYkKMkoheGKPRRL1B451/gMEbTfTSGxNjTIwhJmr0QqIRAhI1EDXq+CFwoXLD6NgYP9Z1hZatPx4vemjZBAeE9due87ySk317tmzPnn33afvt6TkFTnghfyyV4djIJKOZKwCEBO5Z2slgsov+RTH6F8VILoqy3BvX4wFNqayMZfKkJvKk0jlGJnKk0nlSEzlS6RzjU9MAdLS28OTaJM+tH2D9ysSC/e0WcilmJbAb2A88BGwEVszdp6qlG30vC/Zbk748w3fHx9h15AxDw2kANqxK8MVrm27qcEnTmHIzRY6mJjl0Ks3QcJrDw2my05VQ7WwLs6gjQnds9tYVjdAV++/+7liEeDRMJl/wAnuGC5emq9v57OzbVy8ac1Us0sK9yzoZ7O9iMNnlXTWs08nJz85lr1RD/uhIhpPjWcanrlCeE1Wd0XA15JPdUS/8o/R3x1jWXXlRslBSCqUyxZJSKJcpFMsUy5WrpRVLSrFUro4LpTIXL89UQzs1kWd0Ml+984XKnU2yO8aKRIyBnnYGEu2sXtLBo/ctqcuziwU9jl1E+oHNwF5Vzdxo341YsN++0xdz/PDXONvWJBf8rI2mvkpl5Z/xLEPDaU6OZ5nKF8jM2opM5Qu3tJYdEujpaKM33kpfZxu98cp4cbw2HuhpZ9XijoZ+kFAslTmXnWZ0Ms+ZyTxjmSuMTuYZnax8HMvkSecKd+Rn9cZbWZFo94K7FuADPTGS3TGnr4/YG5SM8SFVZbpYnh34uUL1hcyuaKQS2J2t9MbbSLS3NnRg30m5mWI18MenphEgEg4RCQnhllD1ovbhkHj7Q0TCQjhU+Vy4JUSiPdLQ6/oL+eKpMcYRESEaaSEaaWmYUxw3ivbWMKv74qzui7suxbnmOubKGGPMvCzYjTHGZyzYjTHGZyzYjTHGZyzYjTHGZyzYjTHGZyzYjTHGZyzYjTHGZyzYjTHGZyzYjTHGZ5ycK0ZEzgO3cwmlXuDCHS6nWVkvaqwXNdaLGj/2YqWq9s33RU6C/XaJyNDNnAAnCKwXNdaLGutFTZB7YUsxxhjjMxbsxhjjM80W7B+5LqCBWC9qrBc11ouawPaiqdbYjTHGzK/ZHrEbY4yZhwW7Mcb4TNMEu4h8LCK/isibrmtxRUTCInJaRH7ytjWua3JFRJaKyEFvHBGRb0XkZxHZ7rq2eprTh+UiMnLN/Jj3eGe/EJFuEdkjIvtE5GsRaQ1yZjRFsIvIM0CLqm4C7hKRu13X5Mha4HNV3eJtx10X5IKIJICdQIe363XgkKo+DDwrIp3Oiquj6/ThQeCda+bHeXfV1d2LwHuquhU4C7xAgDOjKYId2AJ86Y33AZvdleLURuApEfndezQS1IuRl4DngSnv9hZq8+MAEJQ3pcztw0bgVRE5LCLvuiur/lT1A1X93rvZB7xEgDOjWYK9AzjjjSeApQ5rcekP4DFVfQCIANsc1+OEqk6pauaaXYGcH9fpwx4qd3IbgE0istZJYQ6JyCYgAaQI4Jy4qlmC/RIQ88ZxmqfuO+2Yqo554yEgUE8v/4fNj4pfVDWrqiXgCAGbHyLSA7wPbCfgc6JZftlD1J5KrQNOuSvFqU9FZJ2ItABPA3+6LqhB2Pyo2CsiSRFpB7YCJ1wXVC8i0gp8BbyhqsMEfE40yxrtLuCgiPQDT1BZSwyit4HPAAG+UdX9jutpFDuB3SLyCDAI/Oa4HlfeAn4EZoAPVfVvx/XU0yvA/cAOEdkBfAK8HNTMaJp3nnpHADwOHFDVs67rMY3F+wfeDOyds+5sAirImdE0wW6MMebmNMsauzHGmJtkwW6MMT5jwW6MMT5jwW6MMT5jwW6MMT7zL7EPlsvTSuT5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df5.index\n",
    "y1 = df5[['res_time_min']]\n",
    "y2 = df5[['res_time_max']]\n",
    "y3 = df5[['res_time_avg']]\n",
    "plt.plot(x,y1,label='res_time_min')\n",
    "plt.plot(x,y2,label='res_time_max')\n",
    "plt.plot(x,y3,label='res_time_avg')\n",
    "plt.legend()\n",
    "plt.axis([-1,24,80,320])\n",
    "\n",
    "plt.title('一天中api响应时间')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:00:00</th>\n",
       "      <td>3.169811</td>\n",
       "      <td>494.613585</td>\n",
       "      <td>117.449245</td>\n",
       "      <td>197.782642</td>\n",
       "      <td>152.754717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 01:00:00</th>\n",
       "      <td>2.047619</td>\n",
       "      <td>277.711429</td>\n",
       "      <td>120.772381</td>\n",
       "      <td>147.588571</td>\n",
       "      <td>133.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 02:00:00</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>98.760000</td>\n",
       "      <td>98.760000</td>\n",
       "      <td>98.760000</td>\n",
       "      <td>98.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 10:00:00</th>\n",
       "      <td>1.200000</td>\n",
       "      <td>161.612000</td>\n",
       "      <td>134.490000</td>\n",
       "      <td>137.460000</td>\n",
       "      <td>135.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 11:00:00</th>\n",
       "      <td>2.395349</td>\n",
       "      <td>333.281628</td>\n",
       "      <td>116.090000</td>\n",
       "      <td>154.464884</td>\n",
       "      <td>134.232558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count  res_time_sum  res_time_min  res_time_max  \\\n",
       "2017-11-01 00:00:00  3.169811    494.613585    117.449245    197.782642   \n",
       "2017-11-01 01:00:00  2.047619    277.711429    120.772381    147.588571   \n",
       "2017-11-01 02:00:00  1.000000     98.760000     98.760000     98.760000   \n",
       "2017-11-01 10:00:00  1.200000    161.612000    134.490000    137.460000   \n",
       "2017-11-01 11:00:00  2.395349    333.281628    116.090000    154.464884   \n",
       "\n",
       "                     res_time_avg  \n",
       "2017-11-01 00:00:00    152.754717  \n",
       "2017-11-01 01:00:00    133.333333  \n",
       "2017-11-01 02:00:00     98.000000  \n",
       "2017-11-01 10:00:00    135.400000  \n",
       "2017-11-01 11:00:00    134.232558  "
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6 = df4[['count','res_time_sum','res_time_min','res_time_max','res_time_avg']].groupby(by=df4.index.strftime('%Y-%m-%d %H')).mean()\n",
    "i = pd.to_datetime(df6.index)\n",
    "# df4 = df3.set_index('created_at')\n",
    "df6.index = i\n",
    "df6.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:00:00</th>\n",
       "      <td>3.169811</td>\n",
       "      <td>494.613585</td>\n",
       "      <td>117.449245</td>\n",
       "      <td>197.782642</td>\n",
       "      <td>152.754717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 01:00:00</th>\n",
       "      <td>2.047619</td>\n",
       "      <td>277.711429</td>\n",
       "      <td>120.772381</td>\n",
       "      <td>147.588571</td>\n",
       "      <td>133.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 02:00:00</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>98.760000</td>\n",
       "      <td>98.760000</td>\n",
       "      <td>98.760000</td>\n",
       "      <td>98.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 10:00:00</th>\n",
       "      <td>1.200000</td>\n",
       "      <td>161.612000</td>\n",
       "      <td>134.490000</td>\n",
       "      <td>137.460000</td>\n",
       "      <td>135.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 11:00:00</th>\n",
       "      <td>2.395349</td>\n",
       "      <td>333.281628</td>\n",
       "      <td>116.090000</td>\n",
       "      <td>154.464884</td>\n",
       "      <td>134.232558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count  res_time_sum  res_time_min  res_time_max  \\\n",
       "2017-11-01 00:00:00  3.169811    494.613585    117.449245    197.782642   \n",
       "2017-11-01 01:00:00  2.047619    277.711429    120.772381    147.588571   \n",
       "2017-11-01 02:00:00  1.000000     98.760000     98.760000     98.760000   \n",
       "2017-11-01 10:00:00  1.200000    161.612000    134.490000    137.460000   \n",
       "2017-11-01 11:00:00  2.395349    333.281628    116.090000    154.464884   \n",
       "\n",
       "                     res_time_avg  \n",
       "2017-11-01 00:00:00    152.754717  \n",
       "2017-11-01 01:00:00    133.333333  \n",
       "2017-11-01 02:00:00     98.000000  \n",
       "2017-11-01 10:00:00    135.400000  \n",
       "2017-11-01 11:00:00    134.232558  "
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6['2017-11-01':'2017-11-11'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x4 = df6['2017-11-01':'2017-11-11'].index\n",
    "y4 = df6['2017-11-01':'2017-11-11'][['count']]\n",
    "plt.plot(x4,y4)\n",
    "plt.title('多天api调用次数')\n",
    "# import matplotlib.dates as mdate\n",
    "# ax = plt.gca()   #表明设置图片的各个轴，plt.gcf()表示图片本身\n",
    "# ax.xaxis.set_major_formatter(mdate.DateFormatter('%Y-%m-%d'))  # 横坐标标签显示的日期格式\n",
    "# plt.xticks(pd.date_range('2017-11-01','2017-11-11',freq='d')) #横坐标日期范围及间隔\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "df7 = df4[(df4.index.weekday==5) | (df4.index.weekday==6)]\n",
    "df8 = df4[(df4.index.weekday!=5) & (df4.index.weekday!=6)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y7 = df7[['count']].groupby(by=df7.index.hour).mean()\n",
    "y8 = df8[['count']].groupby(by=df8.index.hour).mean()\n",
    "x7 = y7.index\n",
    "plt.plot(x7,y7,label='True')\n",
    "plt.plot(x7,y8,label='False')\n",
    "plt.legend(title='weekend')\n",
    "\n",
    "plt.title('周末api响应时间对比')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
